U.S. patent number 10,478,110 [Application Number 15/306,710] was granted by the patent office on 2019-11-19 for method for measuring social relationship using heart rhythm pattern (hrp).
This patent grant is currently assigned to CENTER OF HUMAN-CENTERED INTERACTION FOR COEXISTENCE, SANGMYUNG UNIVERSITY SEOUL INDUSTRY--ACADEMY COOPERATION FOUNDATION. The grantee listed for this patent is CENTER OF HUMAN-CENTERED INTERACTION FOR COEXISTENCE, SANGMYUNG UNIVERSITY SEOUL INDUSTRY-ACADEMY COOPERATION FOUNDATION. Invention is credited to Sung Teac Hwang, Sang In Park, Min Cheol Whang, Myoung Ju Won.
United States Patent |
10,478,110 |
Park , et al. |
November 19, 2019 |
Method for measuring social relationship using heart rhythm pattern
(HRP)
Abstract
A social intimacy determining method includes detecting
electrocardiogram (ECG) data from at least two subjects; detecting
heart rhythm pattern (HRP) data from ECG signals of the two
subjects; and determining a relationship (intimacy) between the two
subjects by comparing the HRP data of the two subjects.
Inventors: |
Park; Sang In (Seoul,
KR), Whang; Min Cheol (Gyeonggi-do, KR),
Won; Myoung Ju (Chungcheongnam-do, KR), Hwang; Sung
Teac (Seoul, KR) |
Applicant: |
Name |
City |
State |
Country |
Type |
SANGMYUNG UNIVERSITY SEOUL INDUSTRY-ACADEMY COOPERATION
FOUNDATION
CENTER OF HUMAN-CENTERED INTERACTION FOR COEXISTENCE |
Seoul
Seoul |
N/A
N/A |
KR
KR |
|
|
Assignee: |
SANGMYUNG UNIVERSITY SEOUL
INDUSTRY--ACADEMY COOPERATION FOUNDATION (KR)
CENTER OF HUMAN-CENTERED INTERACTION FOR COEXISTENCE
(KR)
|
Family
ID: |
54935662 |
Appl.
No.: |
15/306,710 |
Filed: |
June 19, 2014 |
PCT
Filed: |
June 19, 2014 |
PCT No.: |
PCT/KR2014/005399 |
371(c)(1),(2),(4) Date: |
October 25, 2016 |
PCT
Pub. No.: |
WO2015/194690 |
PCT
Pub. Date: |
December 23, 2015 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20170049375 A1 |
Feb 23, 2017 |
|
Foreign Application Priority Data
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|
|
|
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Jun 18, 2014 [KR] |
|
|
10-2014-0074511 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B
5/742 (20130101); G16H 40/63 (20180101); A61B
5/04012 (20130101); A61B 5/7264 (20130101); G16H
50/30 (20180101); A61B 5/165 (20130101); A61B
5/0456 (20130101) |
Current International
Class: |
A61B
5/16 (20060101); A61B 5/04 (20060101); A61B
5/00 (20060101); G16H 40/63 (20180101); G16H
50/30 (20180101); A61B 5/0456 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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07021146 |
|
Jan 1995 |
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JP |
|
2011245316 |
|
Dec 2011 |
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JP |
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0927473 |
|
Nov 2009 |
|
KR |
|
20130129714 |
|
Nov 2013 |
|
KR |
|
Other References
Bennett, et al. (2002). Huygens's clocks. Proceedings: Mathematics,
Physical and Engineering Sciences, 458, 563-579. cited by applicant
.
Schmidt, et al. (2008). Coordination: Neural, behavioral and social
dynamics. Springer. ISBN 9783540744764 DOI
10.1007/978-3-540-74479-5. cited by applicant .
Burgoon, et al. (2007). Interpersonal adaptation: Dyadic
interaction patterns. Cambridge University press. cited by
applicant .
Lakens. (2010). Movement synchrony and perceived entitativity.
Journal of Experimental Social Psychology, 46(5), 701-708. cited by
applicant .
Krueger, et al. (2012). Gestural coupling and social cognition:
Mobius syndrome as a case study. Frontiers in human neuroscience,
6. cited by applicant .
Yun, et al. (2012). Interpersonal body and neural synchronization
as a marker of implicit social interaction. SCI REP-UK, 2(959).
cited by applicant .
Miles, et al. (2010). Too late to coordinate: Contextual influences
on behavioral synchrony. European Journal of Social Psychology,
40(1), 52-60. cited by applicant .
Pan, et al. (1985). A real-time QRS detection algorithm. IEEE
Transactions on Biomedical Engineering, 3, 230-236. cited by
applicant .
International Search Report, International Application No.
PCT/KR2014/005399, dated Mar. 16, 2015. cited by applicant.
|
Primary Examiner: Layno; Carl H
Assistant Examiner: Ghand; Jennifer L
Attorney, Agent or Firm: Perman & Green, LLP
Claims
What is claimed is:
1. A social relationship determining method comprising: detecting,
with an electrocardiogram (ECG) sensor, ECG signals from at least
two subjects; defining, with a data processor, a heart rhythm
pattern (HRP) data spectrum including a beat per minute (BPM) from
the ECG signals of the at least two subjects; extracting, with the
data processor, a difference X value between the beats per minute
(BPM) of the heart rhythm pattern (HRP) data spectrum of the at
least two subjects defined from the ECG signals; extracting, with
the data processor, an r square value via a correlation analysis of
each of the HRP data spectrum; determining, with an analyzer
configured for estimating intimacy coupled to the data processor, a
social relationship between the at least two subjects by using the
following equation: Y=0.00943167*X, where Y is a result value,
wherein the social relationship is determined as being strong when
the r square value is larger than the result value Y, otherwise,
the social relationship is determined as being weak; and
displaying, with a display coupled to the data processor, a
strength of the social relationship based on a comparison of the
result value Y with the r square value.
2. The social relationship determining method of claim 1, wherein
R-peak to R-peak Interval (RRI) data is acquired from the ECG
signals.
3. The social relationship determining method of claim 2, wherein
the HRP data spectrum comprises the BPM, and a standard deviation
normal to normal (SDNN) extracted using a standard deviation of a
normal RRI.
4. The social relationship determining method of claim 3, wherein
the r square value and the difference X value between the BPM of
the at least two subjects are obtained via a correlation analysis
of the HRP data spectrum of the at least two subjects.
5. A social relationship determining system for performing the
method of claim 1, the system comprising: the ECG sensor, the ECG
sensor being configured to extract the ECG signals from the at
least two subjects; the display configured to present a specific
facial expression to at least one of the at least two subjects; the
data processor configured to process the ECG signals of the at
least two subjects obtained by the sensor; and the analyzer
configured to analyze intimacy between the at least two subjects by
analyzing the ECG signals processed by the data processor.
6. The social relationship determining system of claim 5, wherein
the data processor extracts an R-peak to R-peak Interval (RRI) from
the ECG signals.
7. The social relationship determining system of claim 6, wherein
the HRP data spectrum comprises the BPM, and a standard deviation
normal to normal (SDNN) extracted using a standard deviation of a
normal RRI.
8. The social relationship determining system of claim 7, wherein
the analyzer uses the r square value and the difference X value
between the BPM of the at least two subjects which are obtained by
the data processor via a correlation analysis of the HRP data
spectrum of the at least two subjects.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
This application is the National Stage of International Application
No PCT/KR2014/005399, having an International Filing Date of 19
Jun. 2014, which designated the United States of America, and which
International Application was published under PCT Article 21 (2) as
WO Publication No. 2015/194690 A1, and which claims priority from
and the benefit of Korean Application No, 10-2014-0074511, filed on
18 Jun. 2014, the disclosures of which are incorporated herein by
reference in their entirety.
BACKGROUND
1. Field
The presently disclosed embodiment relates to methods of measuring
a social relationship, and more particularly, to an intimacy
measuring method based on a heart rhythm pattern (HRP) and a system
using the intimacy measuring method.
2. Brief Description of Related Developments
Social cognition or social interaction denotes understanding of a
mental state or behavior of a target of communication. A
sympathetic reaction with other people is required to understand
mental states or behaviors of the other people (Krueger, et al.
"Gestural Coupling and Social Cognition: Mobius Syndrome as a Case
Study" Frontiers in Human Neuroscience, Volume 6, Article 81,
April, 2012). Many researches into social cognition or social
interaction have been recently being conducted. In these
researches, synchronization or entrainment is considered as an
important concept Synchronization is a phenomenon in which, when
people socially interact with one another, biorhythms of the people
are harmonized (Yun, at al., "Interpersonal Body and Neural
Synchronization as a marker of Implicit Social Interaction",
Scientific Reports, Volume 2, Article 959, December, 2012). This
synchronization phenomenon may not only occur between people but
also in objects or natural phenomena. For example, when pendulums
of several clocks swing horizontally at different speeds, the
pendulums may swing horizontally in the same direction and at the
same speed due to synchronization (Bennett, et al "Huygens's
Clocks" Proceedings of the Royal Society A; Mathematical, Physical
and Engineering Sciences, Volume 458, issue 2019, March, 2002), and
firefly lights that are twinkling individually are synchronized at
one moment and then simultaneously twinkle together at the same
speed (Buck, et al. "Biology of Synchronous Flashing of Fireflies".
Nature Journal, Volume 211, pp. 562-564, August, 1976).
This synchronization phenomenon occurs among people. A
representative example of the synchronization is a phenomenon in
which two people walk in step with each other at the same interval
(Schmidt, et al. "Coordination: Neural, Behavioral, and Social
Dynamics". Springer-Verlag Berlin and Heidelberg GmbH & Co. KG,
2008; Burgoon et al. "Interpersonal Adaptation: Dyadic Interaction
Patterns", Cambridge University Press, 1995). In a study of Yun, et
al., (2012), synchronizations between finger movements occurred
unconsciously between two subjects were compared. This study
reported that synchronization between finger movements of two
subjects occurred and neural activation of the brain greatly
increased when the two subjects perform a cooperative operation,
compared with when the two subjects do not perform a cooperative
operation. In another study, synchronizations between finger
movements of subjects were compared, and it was reported that
greater synchronization occurred when the fingers of the subjects
move at the same speed than when the fingers of the subjects move
at different speeds (Lakens, Daniel. "Movement Synchrony and
Perceived Entitativity". Journal of Social Psychology, Volume 46,
Issue 5, pp. 701-708, September, 2010). It was reported that this
synchronization of body movements relates to an increase in a
positive relationship between people (Miles, et al. "Too Late to
Coordinate: Contectual Influences on Behavioral Synchrony" European
Journal of Social Psychology, Volume 40, pp. 52-60, November,
2009).
As mentioned above, synchronization of unconscious behaviors causes
not only synchronization of bodies but also synchronization of
biological reactions and a positive effect. However, a sympathetic
reaction is very important in interactions between people, and may
differently appear according to with whom a person maintains a
social relationship and communicates. This social relationship is
socially strong or weak. However, current studies into social
relationships are not considered in synchronization. Accordingly,
it is expected that there is a difference in the degree of
synchronization between physiological reactions according to social
relationships. This is because physiological reactions generated
according to social relationships are unconscious. It has been
recently reported that synchronization of physiological reactions
effectively affects maintenance and increase of a social
relationship.
SUMMARY
The presently disclosed embodiment provides a method of
quantitatively estimating a social relationship via synchronization
between heart rhythms.
The presently disclosed embodiment provides a method of estimating
a social relationship or intimacy between two persons via an
interindividual heart entrainment analysis, and a system that uses
the method.
According to an aspect of the presently disclosed embodiment, there
is provided a social relationship determining method including
detecting electrocardiogram (ECG) data from at least two subjects;
detecting heart rhythm, pattern (ERP) data from ECG signals of the
two subjects; and determining a relationship (intimacy) between the
two subjects by comparing the HRP data of the two subjects.
According to an aspect of the presently disclosed embodiment,
R-peak to R-peak interval (RRI) data may be acquired from the ECG
data.
According to an aspect of the presently disclosed embodiment, the
HRP data may include a beat per minute (PPM) mean, and a SDNN
(standard deviation normal to normal) extracted using a standard
deviation of a normal RRI.
According to an aspect of the presently disclosed embodiment, an r
square value and a BPM mean difference between subjects obtained
via a correlation analysis of an HRP signal including the BPM mean
and the SDNN may be used as variables for determining a degree of
synchronization between subjects.
According to an aspect of the presently disclosed embodiment, when
the BPM mean difference between the subjects is a variable X and
the r square value is a variable Y, it may be determined whether
the subjects are synchronized, based on a critical value function
of a linear equation that satisfies Y=0.00943167*X.
According to another aspect of the presently disclosed embodiment,
there is provided a social relationship determining system for
performing the above-described method, the system including a
sensor configured to extract ECG data from the subjects; a display
configured to present a specific facial expression to at least one
of the subjects; a data processor configured to process the ECG
data of the subjects obtained by the sensor; and an analyzer
configured to analyze intimacy between subjects by analyzing the
data.
The presently disclosed embodiment may estimate a social
relationship between two people via an inter-individual heart
entrainment analysis. The inter-individual heart entrainment
analysis uses the synchronization degree of heart rhythms between
two people. In an intimacy estimating method according to the
presently disclosed embodiment, a social relationship between two
people may be quantitatively estimated, and it is expected that the
estimated social relationship helps to ease a social pathological
phenomenon.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an example of a method of checking an
electrocardiogram (ECG) from measurement data of two subjects in
order to detect heart rhythm coherence (HRC) from the two subjects
according to the presently disclosed embodiment.
FIG. 2 explains a sequence of facial expression presentation and
imitation between subjects when HRC is detected.
FIG. 3 is a flowchart of ECG signal processing for HRC
detection.
FIG. 4 is a graph for explaining HRC variables.
FIG. 5 is a time--beat per minute (BPM) graph showing a result of
an HRC analysis of a leader and a follower in each of a friend
group and a stranger group.
FIGS. 6, 6B, and 6C show statistical analysis results (i.e., a
correlation, a difference of PPM, and a difference of standard
deviation normal to normal (SDNN)) of HRC of a leader and a
follower in each of a friend group and a stranger group.
FIG. 7 is a graph for explaining a rule base for determining a
social relationship (intimacy) due to a heart rhythm pattern (HRP)
variable.
FIG. 8 is a graph showing a result of a rule base verification
using an HRP obtained from a new group for verification.
FIG. 9 is a schematic block diagram of an analysis system according
to the presently disclosed embodiment.
DETAILED DESCRIPTION
A method and system for measuring an interpersonal relationship or
intimacy according to an aspect of the presently disclosed
embodiment will now be described more fully with reference to the
accompanying drawings.
In modern society, people need to contact and communicate with many
other people. The media has often and recently released the story
of people who fail to adapt to this society environment and thus
fall behind. Although not so much serious as in the above case,
anyone has a fear or worry of social relationships. In order to
recover social relationships, the social relationships need to be
quantitatively estimated first, and then solutions thereof may be
suggested.
Via a description of the aspect below, the presently disclosed
embodiment provides a method of quantitatively estimating a social
relationship between people, and may improve social relationships
and address a pathological phenomenon via this method.
1. Subjects
72 university students (36 men and 36 women having average ages of
24.27.+-.2.24) participated in an experiment. The subjects
participated in this experiment were people having relationships
over three or more years and making pairs, and the collected people
have the same sex in order to prevent a sexual effect. Neither of
the subjects had disorder nor disease in cardiovascular and nervous
systems and took sufficient sleeps the day before. Further, the
subjects were prohibited from taking in caffeine, cigarette, and
alcohol that may affect a cardiovascular reaction. Before the
experiment, all of the subjects received a general explanation of
the experiment except for the purpose of the experiment and then
underwent the experiment and got paid a certain amount of money in
return for the experiment.
2. Experiment Method
The subjects participated in the experiment were divided into a
strong social relationship group (friend) and a weak social
relationship group (stranger) based on a relationship period. The
strong social relationship group includes friends having
relationships over three or more years and making pairs, and the
weak social relationship group includes strangers making pairs. To
determine social relationships of collected subjects, a simple
survey asking a birthday, family members, hobbies, and the like was
conducted, and only subjects having passed the survey were
participated in the experiment.
The subjected divided into two groups were also divided into
leaders and followers. As shown in FIG. 1, a leader and a follower,
who are two subjects, sit on comfortable chairs while facing each
other. A distance between the two subjects was fixed to 1 m. The
two subjects were instructed to interact with each other through
facial expressions regarding 6 basic emotions defined by Ekman,
namely, fear, disgust, fear, surprise, anger, and happiness. At
this time, the leader was instructed to make a face according to a
facial expression guideline suggested via a screen, and the
follower was instructed to follow the facial express ion of the
leader, luring the experiment, electrocardiogram (ECG) was measured
to compare the heart rhythms of the two people with each other. As
shown in FIG. 2, a total of experiment tasks include a reference
task of 60 seconds, an introduction task of 90 seconds, a practice
task of 90 seconds, and an imitation task of 240 seconds. A task
rest of 30 seconds was included between adjacent tasks, and the
introduction task and the practice task were included in the
experiment so that the subjects may make natural faces in the
imitation task. The above-described experiment process was
conducted on two groups, subjects were crossed between the two
groups and then the above-described experiment process was
conducted again thereon, and the roles of the leader and the
follower were fixed.
For example, if there are friend groups A (leader a, follower b)
and friend groups B (leader c, follower d) as subject groups, tasks
are performed between groups A, and tasks are performed between the
groups B. This is a task when people are intimate. A leader a in a
subject group A and a follower din a subject group B perform tasks,
and a leader c in the group B and a follower b in the group A
perform tasks. This is a task when people are not intimate. In this
way, a total of 36 groups of subjects were participated in the
experiment, two random groups were bound together and
cross-performed the tasks, and the roles of a leader and a follower
do not change. In other words, a leader in a task with respect to
an intimate group is a leader in a task with respect to a stranger
group.
Each in the overall tasks in FIG. 2 is performed as follows.
Reference Task:
Biometric data of a base line is acquired when no stimulus is
presented, before a stimulus is presented.
Introduction Task:
The type and shape of a facial expression are learned to make a
smooth face in a main task (imitation task).
Task Rest:
A subject takes a rest between tasks in order to minimize a
(remaining) effect of a stimulus presented in a previous task and
to reduce an effect on a stimulus in a next task.
Practice Task:
A facial expression is imitated and practiced to make a smooth face
in the main task.
Task Rest:
A subject takes a rest between tasks in order to minimize a
stimulus effect of a task presented previously and to reduce an
effect on a stimulus in a next task.
Imitation Task:
A leader makes a presented face, and a follower imitates the face
made by the leader. At this time, ECG detection is performed in
real time.
In all of the introduction, practice, and imitation tasks, facial
expressions of 6 basic emotions (i.e., fear, disgust, fear,
surprise, anger, and happiness) are presented. In the introduction
and practice tasks, each facial expression is presented for 10
seconds. In the imitation task, each facial expression is presented
for 35 seconds. 5 seconds of rest is included between facial
expressions. The order in which the 6 facial expressions are
presented is randomly determined, and a facial expression is not
selected but the 6 facial expressions are imitated and
practiced.
3. Analysis Method
An analysis method according to the presently disclosed embodiment
uses an analysis system having a structure as illustrated in FIG.
9. The analysis system according to the presently disclosed
embodiment includes an ECG sensor 10 for detecting an ECG signal
(data) from subjects, a signal processor 20 for pre-processing the
ECG signal, an analyzer 30 for estimating intimacy between the
subjects by detecting heart rhythm coherence (HRC) data from a
pre-processed ECG signal, and a display for presenting a facial
expression to one of the subjects. The display may have a structure
of a single display or a multi-display including a display for
presenting a facial expression and a display for display a result
of the facial expression. The analysis system according to the
presently disclosed embodiment including these elements is entirely
based on a computer, and thus a peripheral device such as a
keyboard, a mouse, or a printer may be selectively added.
The ECG signal (data) was sampled with 500 Hz via a lead-I method.
In the experiment according to the presently disclosed embodiment,
the ECG signal was acquired by amplifying a signal via an MP100
power supply and an ECG 100C amplifier (Biopac systems Inc., USA)
and converting an analog signal into a digital signal via
NI-DAQ-Pad9205 (National instruments, USA). The acquired ECG signal
detected an R-peak via a QRS detection algorithm (Pan, et al. "A
Real-Time QRS Detection Algorithm". IEEE Transactions on Biomedical
Engineering, Volume BME-32, No. 3, pp. 230-236, March, 1985) The
detected R-peak extracted an R-peak to R-peak interval (PRI) by
excluding noise and using a difference between normal B-peak
intervals. For a heart rhythm pattern (HRP) analysis, a beat per
minute (PPM) was calculated via 60/RRI, and a standard deviation
normal to normal (SDNN) was extracted using a standard deviation of
a normal RRI. Detailed signal processing is shown in FIG. 3.
Referring to FIG. 3, after the EGG signal was detected from the
subjects, the EGG signal detected an R-peak via a QRS detection
algorithm (Pan et al., 1985). The detected R-peak extracted an
R-peak to R-peak interval (RRI) by excluding noise and using a
difference between normal R-peak intervals. According to the
presently disclosed embodiment, for an HRP analysis, a BPM was
calculated via 60/RRI, and a BPM mean was extracted from the BPM
and an SDNN was extracted using a standard deviation of a normal
RRI.
According to the presently disclosed embodiment, synchronization of
the heart is analyzed via an HRP between two people, and a social
relationship may be estimated by using the analyzed
synchronization. HRP variables for use in heart synchronization
analysis are an SDNN and a BPM mean. A difference between variables
of two people is calculated, and it is determined that, the smaller
the difference is, the higher synchronization between two signals
is. An r square value is extracted via a correlation analysis of an
HRP signal and utilized as a variable for determining the degree of
synchronization.
FIG. 4 is a graph for explaining variables used in intimacy
recognition.
In the graph of FIG. 4, P1 and P2 indicate a leader and a follower
as two subjects who perform tasks, respectively. An SDNN and a PPM
mean of the two subjects were used as variables, and an r square
(r.sup.2) value was used as a variable by squaring an r
(correlation coefficient) value obtained by correlation-analyzing
signals of the two subjects.
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##EQU00001##
Data of 32 people among 72 subjects participated in the present
experiment was used to generate a rule base, and the remaining 40
subjects were utilized to verify the rule base. The rule base will
be described later.
4. Analysis Result
FIG. 5 shows an example of HRPs of a leader and a follower in each
of a friend group and a stranger group. As can be seen from an HRP
of each group, the friend group has a higher entrainment of an HRP
signal of a leader and a follower and a smaller difference in PPM
than the stranger group.
FIGS. 6A, 6B, and 6C show statistical analysis results of HRPs of a
leader and a follower in each of a friend group and a stranger
group. It was checked from FIG. 6 that an r square (correlation)
according to a correlation analysis of the friend group
statistically significantly increased compared with that of the
stranger group (p<0.001). It was confirmed that a difference
between BPM means of subjects statistically greatly decreased in
the stranger group as compared with the friend group (p<0.001,
namely, reliability of 99.9% or greater). However, it was not
confirmed that there was a statistically significant difference
between differences of SDNN of the friend group and the stranger
group (p>0.05, namely, reliability of 851 or greater).
A rule base capable of distinguishing social relationships via a
variable representing a statistically significant difference
between two groups was made as shown in FIG. 7. Two variables used
in a rule base are an r square and a difference of BPM mean and are
defined as an X axis and a Y axis, and thus data of subjects
participated in the experiment was plotted on an X-Y coordinate. An
equation of a straight line passing through a center of two data
that are the closest to each other among the data of two groups,
for example, (9.645, 0.090) and (0, 0), was deduced as a rule base.
Data above the deduced straight line equation was defined as a
strong social relationship, and data below the deduced straight
line equation was defined as a weak social relationship. The
deduced straight line equation is as follows: Y=0.0094*X [Equation
2]
The above linear equation is a rule base that determines intimacy
by using a critical function (general formula) determined via
experimental data of 32 people.
A result of a verification of a rule base of HRP is as shown in
FIG. 8. As described above, HRP variables were extracted from the
remaining 40 subjects who are not experimented and was used in the
verification of the rule base.
According to the result of the verification, the data of 19 groups
among the data of the total of 20 groups were classified into a
strong social relationship, and only the data of one group was
classified into a weak social relationship (accuracy of a strong
social relationship: (19/20)*100=95%). The data of 19 groups among
the data of the total of 20 groups were classified into a weak
social relationship, and only the data of one group was classified
into a strong social relationship (accuracy of a weak social
relationship: (19/20)*100=951). The data accuracy of the overall 40
groups was verified to be 951 (accuracy: (38/40)*100=95%).
As described above, the presently disclosed embodiment estimates a
social relationship between two people via an inter-individual
heart entrainment analysis. In the inter-individual heart
entrainment analysis, the synchronization degree of heart rhythm
between two people was used, and intimacy estimation may be very
accurately performed. According to the presently disclosed
embodiment, a social relationship between two people may be
quantitatively estimated and may be used to ease or address a
social pathological phenomenon.
While the inventive concept has been particularly shown and
described with reference to exemplary aspects thereof, it will be
understood that various changes in form and details may be made
therein without departing from the spirit and scope of the
following claims.
* * * * *